Hyperspectral demosaicking and crosstalk correction using deep learning

Dijkstra, Klaas; Loosdrecht, Jaap van de; Schomaker, Lambert; Wiering, Marco A.

doi:10.1007/s00138-018-0965-4

Cited by 28 publications

(22 citation statements)

References 29 publications

(39 reference statements)

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“…Increase in the number of layers and the addition of nonlinearity, improved results to achieve a median structural similarity (SSIM) index of 0.86 between original and upscaled images. [140] "An efficient employment of internet of multimedia things in smart and future agriculture" Robustness of the proposed model was higher than the robustness of conventional approaches. [142] "Mapping irrigated areas using Sentinel-1 Time Series in Catalonia, Spain"…”

Section: Video Sequence With 1775 Bees and 98 Visual Mitesmentioning

confidence: 94%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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Section: Video Sequence With 1775 Bees and 98 Visual Mitesmentioning

confidence: 94%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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“…However, their network depends on a vast scale of datasets during training and will change the size of the output image irregularly due to their network design. In addition, there are also some algorithms for hyperspectral and multispectral image demosaicking [ 25 , 26 , 27 ], and among these, Dijkstra’s method [ 26 ] gives an effective CNN-based lightweight demosaicking method and achieves good results.…”

Section: Related Workmentioning

confidence: 99%

A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices

Wang

Zhao

Dou

et al. 2021

Sensors

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Image demosaicking has been an essential and challenging problem among the most crucial steps of image processing behind image sensors. Due to the rapid development of intelligent processors based on deep learning, several demosaicking methods based on a convolutional neural network (CNN) have been proposed. However, it is difficult for their networks to run in real-time on edge computing devices with a large number of model parameters. This paper presents a compact demosaicking neural network based on the UNet++ structure. The network inserts densely connected layer blocks and adopts Gaussian smoothing layers instead of down-sampling operations before the backbone network. The densely connected blocks can extract mosaic image features efficiently by utilizing the correlation between feature maps. Furthermore, the block adopts depthwise separable convolutions to reduce the model parameters; the Gaussian smoothing layer can expand the receptive fields without down-sampling image size and discarding image information. The size constraints on the input and output images can also be relaxed, and the quality of demosaicked images is improved. Experiment results show that the proposed network can improve the running speed by 42% compared with the fastest CNN-based method and achieve comparable reconstruction quality as it on four mainstream datasets. Besides, when we carry out the inference processing on the demosaicked images on typical deep CNN networks, Mobilenet v1 and SSD, the accuracy can also achieve 85.83% (top 5) and 75.44% (mAP), which performs comparably to the existing methods. The proposed network has the highest computing efficiency and lowest parameter number through all methods, demonstrating that it is well suitable for applications on modern edge computing devices.

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“…Within the context of big data, energetic cryptogram data is fast, attractive, supplementary, significant, and pertinent in extrapolative treatment. Dijkstra et al [40] analyzed biosensors' data with the DL to predict heart infections. Biosensors engaged were noninvasive and unruffled of electromagnetic physique infection instruments, temperament rate, and plasma oxygen feeler and electrocardiogram sensors.…”

Section: Role Of DL In Improvingmentioning

confidence: 99%

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

Shafik

Matinkhah

Ahmad

2020

Wireless Communications and Mobile Computing

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The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, and search and rescue operations among others. The current technologies are using fixed base stations to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. This open gate for the UAV technology is to be used as a mobile alternative to increase accessibility with fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general and provides detailed steps that are involved in the multiarmed bandit (MAB) approach in solving the UAV biomedical engineering technology device and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal performance and efficiency of medical engineered devices. The simulated results depicted that a multiarmed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technology systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations.

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Hyperspectral demosaicking and crosstalk correction using deep learning

Cited by 28 publications

References 29 publications

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

A Compact High-Quality Image Demosaicking Neural Network for Edge-Computing Devices

A 5G Beam Selection Machine Learning Algorithm for Unmanned Aerial Vehicle Applications

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